1. Field of the Invention
The present invention relates to forecasting weather-based demand for a product for an entity without a need for the entity to develop sales history data for the product.
2. Background Art
Historical Perspective of Retailing
The retail industry has historically been influenced by the shape of the times. For example, the retail industry is impacted by war and peace, lifestyle changes, demographic shifts, attitude progressions, economic expansion and contraction, tax policies, and currency fluctuations.
The period from 1965 to 1975 was marked by growth and segmentation in the retail industry. New types of stores such as department stores, specialty stores, and discount stores appeared, increasing competition in the retail industry. One result of this growth was a decrease in gross margin (sales price—cost of goods sold). Another result was a shifting of supply sources. Originally, merchandise was supplied exclusively by vendors. However, segmentation and growth resulted in specialty chains and discounters manufacturing merchandise in-house (commonly known as vertical integration).
The period from 1975 to 1980 was marked by disillusionment and complexity in the retail industry. Inflation and women entering the work force in significant numbers resulted in a more sophisticated consumer. Many retailers began to rethink the basics of merchandising in terms of merchandise assortments, store presentations, customer service, and store locations. Other less sophisticated retailers continued on an undisciplined and unstructured policy of store growth.
The period from 1980 to 1990 was marked by recovery and opportunity in the retail industry. An economic boom stimulated consumer confidence and demand. This, coupled with the expansion of the previous period, paved the way for the retail industry to overborrow and overbuild. With their increased size, retailers became increasingly unable to manage and analyze the information flowing into their organizations.
Retailing Problems and the Evolution of Computing Systems
The problems and opportunities facing the retailer fall into two categories of factors: (1) external factors and (2) internal (or industry) factors. External factors impacting the retail industry include, for example, adverse or favorable weather, rising labor costs, increasing property costs, increased competition, economics, increasing cost of capital, increasing consumer awareness, increasing distribution costs, changing demographics and zero population growth, decreasing labor pool, and flat to diminishing per capita income.
Internal (or industry) factors affecting the retail industry include, for example, large number of stores (decentralization), homogeneity among retailers, continuous price promotion (equates to decreased gross margin), decreasing customer loyalty, minimal customer service, physical growth limitations, and large quantities of specific retailer store information.
Growth and profitability can only be achieved by maximizing the productivity and profitability of the primary assets of the retail business: merchandise (inventory), people, and retail space. The above external and industry factors have added to a retailer's burdens of maintaining the productivity of these assets.
Of the three primary assets, merchandise productivity is particularly important due to the limiting effect of external and internal factors on people and space productivity (e.g., physical growth limitations and high labor costs). Merchandise productivity can be best achieved by maintaining effective mix of product in a store by product characteristic (merchandise assortments).
To achieve more effective merchandise assortments, a retailer must have a merchandise plan that provides the retailer with the ability to (1) define, source, acquire, and achieve specific target merchandise assortments for each individual store location; (2) achieve an efficient, non-disruptive flow from supply source to store; (3) maintain store assortments which achieve anticipated financial objectives; and (4) communicate effectively across all areas of the business to facilitate coordinated action and reaction.
Such an effective merchandise plan must consider all possible external and industry factors. To obtain this knowledge, a retailer must have responsive and easy access to the data associated with these factors, referred to as external and industry data, respectively. To assimilate and analyze this data, which comes from many sources and in many formats, retailers began utilizing management information systems (MIS). The primary function of the MIS department in the retail industry has been the electronic collection, storage, retrieval, and manipulation of store information. Mainframe-based systems were primarily utilized due to the large amount of store information generated. Store information includes any recordable event, such as purchasing, receiving, allocation, distribution, customer returns, merchandise transfers, merchandise markdowns, promotional markdowns, advertising, inventory, store traffic, and labor data. In contrast to the extensive collection and storage of internal data, these systems, did not typically process external data. Rather, this non-industry data was simply gathered and provided to the retailer for personal interpretation.
Since understanding of local and region level dynamics is a requisite for increased retailing productivity, retailers would essentially feed store information at the store level into massive mainframe databases for subsequent analysis to identify basic trends. However, the use of mainframes typically requires the expense of a large MIS department to process data requests. There is also an inherent delay from the time of a data request to the time of the actual execution. This structure prevented MIS systems from becoming cost effective for use by executives in making daily decisions, who are typically not computer specialists and thus rely on data requests to MIS specialists.
In the 1970s and 1980s, retrieval of store information for analysis and subsequent report generation were manually or electronically generated through a custom request to MIS department personnel. More recently, in response to the need for a rapid executive interface to data for managerial plan preparation, a large industry developed in Executive Information Systems (EIS). An EIS system is a computer-based system, which typically operates on a personal computer workstation platform and interfaces with the MIS mainframe or mid-range database. EIS systems generally allows information and analysis can be accessed, created, packaged and/or delivered for use on demand by users who are non-technical in background. Also, EIS systems perform specific managerial applications without extensive interaction with the user, which reduces or eliminates the need for computer software training and documentation.
In contrast to store information, external information consists of manual reports covering such topics as economic forecasts, demographic changes, and competitive analysis. In conventional systems, external information is separately made available to the user for consideration in developing managerial plan.
Technical improvements in speed and storage capability of personal computers (PCs) have allowed this trend towards EIS systems to take place, while most firms still maintain a mainframe or minicomputer architecture for basic point of sale (POS) data storage and processing. The advent of powerful mini computers, local area networks (LANs), and PC systems has resulted in many of the traditional mainframe retailing applications migrating to these new platforms.
Weather and Planning Activities
Weather anomalies are more of a regional and local event rather than a national phenomenon in countries as geographically large as the United States. This is not to say that very anomalous weather cannot affect an entire country or continent, creating, for example, abnormally hot or cold seasons. However, these events are less frequent than regional or local aberrations. Significant precipitation and temperature deviations from normal events occur continually at time intervals in specific regions and locations throughout the United States.
Because actual daily occurrences fluctuate around the long term “normal” or “average” trend line (in meteorology, normal is typically based on a 30 year average), past historical averages can be a very poor predictor of future weather on a given day and time at any specific location. Implicitly, weather effects are already embedded in an MIS POS database, so the retailer is consciously or unconsciously using some type of historical weather as a factor in any planning approach that uses trendline forecasts based on historical POS data for a given location and time period.
At a national level, weather is only one of several important variables driving consumer demand for a retailer's products. Several other factors are, for example, price, competition, quality, advertising exposure, and the structure of the retailer's operations (number of stores, square footage, locations, etc). Relative to the national and regional implementation of planning, the impact of all of these variables dominates trendline projections.
As described above, POS databases track sales trends of specific categories at specific locations which are then aggregated and manipulated into regional and national executive information reports. Since the impact of local weather anomalies can be diluted when aggregated to the national levels (sharp local sales fluctuations due to weather tend to average out when aggregated into national numbers), the impact of weather has not received much scrutiny relative to national planning and forecasting.
The impact of weather on a regional and local level is direct and dramatic. At the store level, weather is often a key driver of sales of specific product categories. Weather also influences store traffic which, in turn, often impacts sales of all goods. Weather can influence the timing and intensity of markdowns, and can create stockout situations which replenishment cycles can not address due to the inherent time lag of many replenishment approaches.
The combination of lost sales due to stockouts and markdowns required to move slow inventory are enormous hidden costs, both in terms of lost income and opportunity costs. Aggregate these costs on a national level, and weather is one of the last major areas of retailing where costs can be carved out (eliminate overstocks) and stores can improve productivity (less markdown allows for more margin within the same square footage).
In short, weather can create windows of opportunity or potential pitfalls that are completely independent events relative to economics, demographics, consumer income, and competitive issues (price, quality). The cash and opportunity costs in the aggregate are enormous.
Conventional Approaches Addressing Weather Impact
Though the majority of retailers acknowledge the effects of weather, many do not consider weather as a problem per se, considering it as a completely uncontrollable part of the external environment.
However, the underlying problem is essentially one of prediction of the future; i.e., developing a predictive model. All retailers must forecast (informally or formally) how much inventory to buy and distribute based on expected demand and appropriate inventory buffers. Hence, many conventional predictive modeling processes have been developed, none of which adequately address the impact of weather.
One conventional solution is to purposely not consider the impact of weather on retail sales. In such instances, the retailer will maintain high inventory levels and rapidly replenish the inventory as it is sold. This approach creates large working capital needs to support such a large inventory.
Another conventional solution is for the retailer to qualitatively use weather information to anticipate future demands. This procedure, if used by decision makers, is very subjective and does not evaluate weather in a predictive sense. Nor does it quantify the effect of past and future weather on consumer demands.
Another conventional approach is the utilization of climatology. Climatology is the study of the climates found on the earth. Climatology synthesizes weather elements (temperature, precipitation, wind, etc.) over a long period of time (years), resulting in characteristic weather patterns for a given area for a given time frame (weekly, monthly, seasonably, etc.). This approach does not utilize forecasted weather as a parameter, which can vary considerably from any given time period from year to year for a given area. Climatology yields only the average weather condition, and is not indicative of the weather for any specific future time frame.
Manufacturers and retailers have been known to rely on broad projections developed by the National Weather Service (the governmental entity in the USA charged with disseminating weather data to the public) and other private forecasting firms. With reference to long range projections, these may be vague, broad, and lack regional or local specificity. It is of limited use since they are issued to cover anticipated weather averaged for 30, 60, or 90 day periods covering large geographic areas. This information cannot be quantified or easily integrated into an MIS-based planning system which is geared toward a daily or weekly time increment for specific location and time.
In summary, the above conventional solutions to weather planning problems in retail all suffer from one or several deficiencies which severely limit their commercial value, by not providing: (1) regional and/or local specificity in measuring past weather impact and projecting future weather impact, (2) the daily, weekly, and monthly increment of planning and forecasting required in the retail industry, (3) ample forecast leadtime required by such planning applications as buying, advertising, promotion, distribution, financial budgeting, labor scheduling, and store traffic analysis, (4) the quantification of weather impact required for precise planning applications such as unit buying and unit distribution, financial budget forecasting, and labor scheduling, (5) reliability beyond a three to five day leadtime, (6) a predictive weather impact model, which links quantitative weather impact measurement through historical correlation, with quantitative forecasts, (7) the ability to remove historical weather effects from past retail sales for use as a baseline in sales forecasting, (8) an entirely electronic, computerized, EIS implementation for ease of data retrieval/analysis with specific functions that solve specific managerial planning applications, and (9) a graphical user interface representing a predictive model in graphs, formats, and charts immediately useful to the specific managerial applications.
Today's Additional Needs
The above-identified nine limitations of conventional solutions to weather planning problems in retail were met by such systems, methods and computer program products disclosed in commonly-assigned U.S. Pat. Nos. 5,521,813; 5,491,629; 5,796,932; and 5,832,456; and commonly-assigned U.S. application Ser. Nos. 09/656,397; and 09/907,714, each of which is incorporated herein in its entirety by reference. However, what is currently needed is a system, method, or computer program product that can forecast weather-based demand for a product for an entity without a need for the entity to develop sales history data for the product.
Scope of the Problem
While the above discussion focused on the retail industry (i.e., the impact of weather on the retail industry), the effects of weather are not confined to the retail industry. Weather impacts many aspects of human endeavor. Accordingly, the discussion above applies equally to many other applications, including but not limited to, retail products and services; manufacturing/production (e.g., construction, utilities, movie production companies, advertising agencies, forestry, mining, and the like); transportation; the entertainment industry; the restaurant industry; consumer activities and/or events (e.g., golfing, skiing, fishing, boating, vacations, family reunions, weddings, honeymoons, and the like); and processing, valuating, and trading of financial instruments (e.g., options, futures, swaps, and the like).